High resolution spatially resolved transcriptomic atlas of kidney injury and repair by RNA hybridization-based in situ sequencing [10X Visium]
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE227045 | GEO | 2024/01/17
REPOSITORIES: GEO
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Project description:Spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ. We applied direct RNA hybridization-based in situ sequencing (ISS, Cartana) to compare male and female healthy mouse kidneys and the male kidneys injury and repair timecourse of ischemic reperfusion injury (IRI). A pre-selected panel of 200 genes were used to identify the dynamics of cell states and their spatial distributions during injury and repair. We developed a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting atlas allowed us to resolve distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. Projection of snRNA-seq dataset from the same injury and repair samples allowed us to impute the spatial localization of genes not directly measured by Cartana.
2024-01-17 | GSE227044 | GEO
Project description:In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to define the mechanisms controlling cell fate during migration, proliferation, and differentiation following tissue injury and thereby reexamine the canonical phases of wound healing. We show that wounding triggers a polyclonal proliferation of tissue-resident, mechano-responsive fibroblasts, the subpopulations of which inhabit spatially-distinct regions within the wound and harbor distinct activities. These findings have broad implications for the study of tissue repair in complex organ systems.
2021-06-24 | GSE178758 | GEO
Project description:High resolution spatially resolved transcriptomic atlas of kidney injury and repair by RNA hybridization-based in situ sequencing
| PRJNA942943 | ENA
Project description:Fibroblasts are present in every organ. While the role fibroblasts in chronic diseases such as fibrosis or tumor expression has been extensively explored, little is known about their physiological role. The kidney possesses a unique capacity to recover from even severe acute injury. We study molecular mechanisms of this intrinsic repair capacity in the mouse model of ischemia-reperfusion (IR). In this model, the renal artery and vein are clamped for 45 min, leading to acute kidney injury. The kidney spontaneously recovers from such IR injury within 14 days. IR kidney injury is associated with a transient accumulation of fibroblasts in the diseased tissue. We hypothesized that fibroblasts aid the repair of acute IR injury in the kidney. To elucidate how FSP1+ fibroblasts may contribute to the repair of kidney injury, we undertook a global unbiased approach to compare gene expression profiles of fibroblasts isolated from kidneys post-IRI and from control kidneys by FACS sorting. To investigate the role fibroblasts may play in the repair of kidney inhury, we performed ischemia reperfusion injury surgery on transgenic mice in which the FSP-1 promoter drives EGFP expression. Kidney injury peaks at day 3 post-IRI, followed by spontaneous regeration that restores nearly perfect kidney architecture and health by day 10. Fibroblasts are thought to possibly play a role in this process, as they are normally rare in the healthy kidney, acute kidney injury is associated with a transient accumulation of interstitial fibroblasts, but whether they may help repair the acute kidney injury or in fact could contribute to the damage is not known. To compare the gene expression profiles of normal fibroblasts and those from post-ischemic kidneys, we sacrificed control FSP1-GFP mice and the FSP1-GFP mice three days post-IRI. We generated single-cell suspensions from both the post-IRI and control kidneys, and then isolated FSP1-GFP+ cells by FACS sorting that, when cultured on plastic, displayed typical fibroblast morphology. Total RNA was immediately extracted from the sorted cells and amplified to produce enough for a array. We biotinylated five of the samples from post-ischemic kidneys and three of the control (non-ischemic) kidneys and used Affymetrix 3' Arrays to examine differences in gene expression profiles between the two groups that may she some light on what role, if any, fibroblasts play in the spontaneous healing of the kidney after acute kidney injury. We performed ischemia reperfusion surgery in FSP1-GFP mice, and at day 3, we sacrificed the mice, isolated FSP1-GFP positive cells from both IR and normal control kidneys by FACS sorting, extracted total RNA from the isolated FSP1-GFP cells and used Affymetrix Mouse Expression Array 430 2.0 microarrays to perform gene expression profiling of the samples. All told, we performed the FACS Sorting, RNA extration, and hybridization with 5 ischemic kidneys and 3 normal kidneys. Fibroblasts, acute kidney injury, repair, comparative gene expression profiling, microarrays, FACS sorting, role in healing
2014-10-28 | E-GEOD-62732 | biostudies-arrayexpress
Project description:In this study, we present a multiplexed version of Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm2 with spots of 50 µm×50 µm. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared with previous reports. Furthermore, the introduction of alignment markers allows seamless registration of images and spatial transcriptomic spot coordinates. Together with technological advances, we provide an open-source computational pipeline to transform raw sequencing data from xDBiT experiments into the AnnData format. The functionality of xDBiT was demonstrated by the acquisition of 16 spatially resolved transcriptomic datasets from five different murine organs, including cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of xDBiT spatial transcriptomes allowed for in-depth characterization of the murine kidney.
2023-02-09 | GSE207843 | GEO
Project description:Kidneys possess come one of the most intricate three-dimensional cellular structures in the body, yet the spatial molecular principles of kidney health and disease remain inadequately understood. Here we generated high-quality single cell (sc), single nuclear (sn), spatial (sp) RNA expression and sn open chromatin datasets for 79 samples, capturing half a million cells from healthy, diabetic, and hypertensive diseased human kidneys. By combining the sn/sc and sp RNA data, we identify over 100 cell types and states and successfully map them back to their spatial locations. Computational deconvolution of spRNA-seq helps to identifies glomerular, tubular, immune, and fibrotic spatial microenvironments (FMEs). Although injured proximal tubule cells appear to be the nidus of fibrosis, we reveal the complex, heterogenous cellular and spatial organization of human FMEs, including the highly intricate and organized immune environment. We demonstrate the clinical utility of the FME spatial gene signature for the classification of a large number of human kidneys for disease severity and prognosis. We provide a comprehensive spatially-resolved molecular roadmap for the human kidney and the fibrotic process and demonstrate the clinical utility of spatial transcriptomics.
2024-01-17 | GSE211785 | GEO
Project description:Spatial omics emerged as a new frontier of biological and biomedical research. Here, we present spatial-CUT&Tag for spatially resolved genome-wide profiling of histone modifications by combining in situ CUT&Tag chemistry, microfluidic deterministic barcoding, and next-generation sequencing. Spatially resolved chromatin states in mouse embryos revealed tissue-type-specific epigenetic regulations in concordance with ENCODE references and provide spatial information at tissue scale. Spatial-CUT&Tag revealed epigenetic control of the cortical layer development and spatial patterning of cell types determined by histone modification in mouse brain. Single-cell epigenomes can be derived in situ by identifying 20-micrometer pixels containing only one nucleus using immunofluorescence imaging. Spatial chromatin modification profiling in tissue may offer new opportunities to study epigenetic regulation, cell function, and fate decision in normal physiology and pathogenesis.
2022-02-10 | GSE165217 | GEO
Project description:Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (45 donors) and diseased (48 patients) kidneys. This has provided a high resolution cellular atlas of 51 main cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations spanning the entire kidney. We further define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states. Molecular signatures permitted localization of these states within injury neighborhoods using spatial transcriptomics, while large-scale 3D imaging analysis (~1.2 million neighborhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents the most comprehensive benchmark of cellular states, neighborhoods, outcome-associated signatures, and publicly available interactive visualizations.
2022-10-10 | GSE183274 | GEO
Project description:Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (45 donors) and diseased (48 patients) kidneys. This has provided a high resolution cellular atlas of 51 main cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations spanning the entire kidney. We further define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states. Molecular signatures permitted localization of these states within injury neighborhoods using spatial transcriptomics, while large-scale 3D imaging analysis (~1.2 million neighborhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents the most comprehensive benchmark of cellular states, neighborhoods, outcome-associated signatures, and publicly available interactive visualizations.
2022-10-10 | GSE183273 | GEO
Project description:Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (45 donors) and diseased (48 patients) kidneys. This has provided a high resolution cellular atlas of 51 main cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations spanning the entire kidney. We further define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states. Molecular signatures permitted localization of these states within injury neighborhoods using spatial transcriptomics, while large-scale 3D imaging analysis (~1.2 million neighborhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents the most comprehensive benchmark of cellular states, neighborhoods, outcome-associated signatures, and publicly available interactive visualizations.
2022-10-10 | GSE183277 | GEO